Transcriptomics in 3D Cellular Models

Transcriptomics is a branch of ‘omics’ technology which involves the study of RNA molecules in the cell. It provides an insight into how genes are expressed and interconnected. The transcriptome is highly susceptible to changes and/or stresses in the internal and external environment including disease state and drug-related effects. As a consequence, interest in transcriptomics has grown massively in the field of toxicology where it has shown significant power in the prediction of toxicity risk and can provide an in-depth mechanistic understanding of specific drug-induced adverse effects.

The introduction of RNA-seq has greatly advanced the field. RNA-Seq uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment, analysing the continuously changing cellular transcriptome.  Our current research has combined organotypic 3D liver models with high throughput 384 well plate based RNA-seq. In order to obtain full transcriptome dose response data, high content imaging (HCI) analysis is used to select the top concentration for dosing. As RNA-seq provides a much broader understanding of the whole transcriptome including indications of the off-target mode of action (MOA), large complex data sets are created. As such, it is critical to have the appropriate computational tools to analyse differentially expressed gene signatures (DEGs) and interpret the associated perturbed pathways linked to different types of toxic mechanism.

A sophisticated platform known as PanHunter has been developed for this purpose to not only handle the data from in-house RNA-seq but also allow integration with other internal and external data sources, including gene metadata.

Expert, Alicia Rosell-Hidalgo PhD, talks about our latest research in this exciting field using RNA-seq technology in 3D cellular models. The webinar covers:

  • an overview to drug induced toxicity with a focus on DILI
  • the advantages and limitations of the different in vitro liver models for toxicology studies
  • the use of HCS and 3D liver models as a preliminary assessment of DILI prior to RNA-seq 
  • the power of high throughput RNA-seq and how this can be coupled with sophisticated data analysis, machine learning and artificial intelligence to predict toxicity and understand specific mechanisms of toxicity

Watch the webinar

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